{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "binding-protest",
   "metadata": {},
   "source": [
    "# DoWhy: Interpreters for Causal Estimators\n",
    "\n",
    "This is a quick introduction to the use of interpreters in the DoWhy causal inference library.\n",
    "We will load in a sample dataset, use different methods for estimating the causal effect of a (pre-specified)treatment variable on a (pre-specified) outcome variable and demonstrate how to interpret the obtained results.\n",
    "\n",
    "First, let us add the required path for Python to find the DoWhy code and load all required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "natural-library",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "indirect-statistics",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import logging\n",
    "\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brave-yukon",
   "metadata": {},
   "source": [
    "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome.\n",
    "\n",
    "Beta is the true causal effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "incident-auction",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "6257\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       Z0        Z1        W0        W1        W2        W3 W4     v0  \\\n",
       "0     0.0  0.500477 -2.263934 -0.884065  1.477420  1.220963  1  False   \n",
       "1     0.0  0.147629 -2.335237  0.236077  1.608586 -0.215191  2   True   \n",
       "2     0.0  0.328339 -0.647024  0.013990  1.262352 -1.293091  2   True   \n",
       "3     0.0  0.351314 -0.926838  1.167099 -0.457953 -1.377098  2  False   \n",
       "4     0.0  0.322452 -0.238075  1.502104  0.043516 -1.987510  2   True   \n",
       "...   ...       ...       ...       ...       ...       ... ..    ...   \n",
       "9995  0.0  0.494717 -1.508109 -0.813709 -2.108048 -0.587423  3   True   \n",
       "9996  1.0  0.215997 -2.036712  1.844353  3.218106  0.543036  3   True   \n",
       "9997  0.0  0.688605 -0.319733  0.131430  0.464761 -0.534403  3   True   \n",
       "9998  1.0  0.374325 -0.364926  0.445597  1.431352 -0.353107  0   True   \n",
       "9999  1.0  0.440766 -1.553496  0.807450 -0.905183 -1.605284  1   True   \n",
       "\n",
       "             y  \n",
       "0     0.259401  \n",
       "1     1.438495  \n",
       "2     1.558208  \n",
       "3     0.230522  \n",
       "4     1.410322  \n",
       "...        ...  \n",
       "9995  1.238320  \n",
       "9996  2.385722  \n",
       "9997  2.009345  \n",
       "9998  1.089850  \n",
       "9999  0.593611  \n",
       "\n",
       "[10000 rows x 9 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Z0</th>\n      <th>Z1</th>\n      <th>W0</th>\n      <th>W1</th>\n      <th>W2</th>\n      <th>W3</th>\n      <th>W4</th>\n      <th>v0</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.500477</td>\n      <td>-2.263934</td>\n      <td>-0.884065</td>\n      <td>1.477420</td>\n      <td>1.220963</td>\n      <td>1</td>\n      <td>False</td>\n      <td>0.259401</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.147629</td>\n      <td>-2.335237</td>\n      <td>0.236077</td>\n      <td>1.608586</td>\n      <td>-0.215191</td>\n      <td>2</td>\n      <td>True</td>\n      <td>1.438495</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.328339</td>\n      <td>-0.647024</td>\n      <td>0.013990</td>\n      <td>1.262352</td>\n      <td>-1.293091</td>\n      <td>2</td>\n      <td>True</td>\n      <td>1.558208</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.351314</td>\n      <td>-0.926838</td>\n      <td>1.167099</td>\n      <td>-0.457953</td>\n      <td>-1.377098</td>\n      <td>2</td>\n      <td>False</td>\n      <td>0.230522</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.322452</td>\n      <td>-0.238075</td>\n      <td>1.502104</td>\n      <td>0.043516</td>\n      <td>-1.987510</td>\n      <td>2</td>\n      <td>True</td>\n      <td>1.410322</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>0.0</td>\n      <td>0.494717</td>\n      <td>-1.508109</td>\n      <td>-0.813709</td>\n      <td>-2.108048</td>\n      <td>-0.587423</td>\n      <td>3</td>\n      <td>True</td>\n      <td>1.238320</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>1.0</td>\n      <td>0.215997</td>\n      <td>-2.036712</td>\n      <td>1.844353</td>\n      <td>3.218106</td>\n      <td>0.543036</td>\n      <td>3</td>\n      <td>True</td>\n      <td>2.385722</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>0.0</td>\n      <td>0.688605</td>\n      <td>-0.319733</td>\n      <td>0.131430</td>\n      <td>0.464761</td>\n      <td>-0.534403</td>\n      <td>3</td>\n      <td>True</td>\n      <td>2.009345</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>1.0</td>\n      <td>0.374325</td>\n      <td>-0.364926</td>\n      <td>0.445597</td>\n      <td>1.431352</td>\n      <td>-0.353107</td>\n      <td>0</td>\n      <td>True</td>\n      <td>1.089850</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>1.0</td>\n      <td>0.440766</td>\n      <td>-1.553496</td>\n      <td>0.807450</td>\n      <td>-0.905183</td>\n      <td>-1.605284</td>\n      <td>1</td>\n      <td>True</td>\n      <td>0.593611</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 125
    }
   ],
   "source": [
    "data = dowhy.datasets.linear_dataset(beta=1,\n",
    "        num_common_causes=5, \n",
    "        num_instruments = 2,\n",
    "        num_treatments=1,\n",
    "        num_discrete_common_causes=1,\n",
    "        num_samples=10000,\n",
    "        treatment_is_binary=True,\n",
    "        outcome_is_binary=False)\n",
    "df = data[\"df\"]\n",
    "print(df[df.v0==True].shape[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "approved-conflict",
   "metadata": {},
   "source": [
    "Note that we are using a pandas dataframe to load the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-exercise",
   "metadata": {},
   "source": [
    "## Identifying the causal estimand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oriented-smell",
   "metadata": {},
   "source": [
    "We now input a causal graph in the GML graph format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "streaming-sweden",
   "metadata": {},
   "outputs": [],
   "source": [
    "# With graph\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment=data[\"treatment_name\"],\n",
    "        outcome=data[\"outcome_name\"],\n",
    "        graph=data[\"gml_graph\"],\n",
    "        instruments=data[\"instrument_names\"]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "destroyed-cameroon",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "juvenile-segment",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "purple-discretion",
   "metadata": {},
   "source": [
    "We get a causal graph. Now identification and estimation is done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "comparative-franchise",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Estimand type: nonparametric-ate\n\n### Estimand : 1\nEstimand name: backdoor\nEstimand expression:\n  d                                       \n─────(Expectation(y|W1,W3,Z1,W2,W4,Z0,W0))\nd[v₀]                                     \nEstimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,Z1,W2,W4,Z0,W0,U) = P(y|v0,W1,W3,Z1,W2,W4,Z0,W0)\n\n### Estimand : 2\nEstimand name: iv\nEstimand expression:\nExpectation(Derivative(y, [Z1, Z0])*Derivative([v0], [Z1, Z0])**(-1))\nEstimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\nEstimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n\n### Estimand : 3\nEstimand name: frontdoor\nNo such variable found!\n\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "better-interim",
   "metadata": {},
   "source": [
    "## Method 1: Propensity Score Stratification\n",
    "\n",
    "We will be using propensity scores to stratify units in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "certified-iraqi",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                       \n",
      "─────(Expectation(y|W1,W3,Z1,W2,W4,Z0,W0))\n",
      "d[v₀]                                     \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,Z1,W2,W4,Z0,W0,U) = P(y|v0,W1,W3,Z1,W2,W4,Z0,W0)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W3+Z1+W2+W4+Z0+W0\n",
      "Target units: att\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.9937614288925221\n",
      "\n",
      "Causal Estimate is 0.9937614288925221\n"
     ]
    }
   ],
   "source": [
    "causal_estimate_strat = model.estimate_effect(identified_estimand,\n",
    "                                              method_name=\"backdoor.propensity_score_stratification\",\n",
    "                                              target_units=\"att\")\n",
    "print(causal_estimate_strat)\n",
    "print(\"Causal Estimate is \" + str(causal_estimate_strat.value))"
   ]
  },
  {
   "source": [
    "### Textual Interpreter\n",
    "\n",
    "The textual Interpreter describes (in words) the effect of unit change in the treatment variable on the outcome variable."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "painful-reynolds",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9937614288925221 in the expected value of the outcome [y], over the data distribution/population represented by the dataset.\n"
     ]
    }
   ],
   "source": [
    "# Textual Interpreter\n",
    "interpretation = causal_estimate_strat.interpret(method_name=\"textual_effect_interpreter\")"
   ]
  },
  {
   "source": [
    "### Visual Interpreter\n",
    "\n",
    "The visual interpreter plots the change in the standardized mean difference (SMD) before and after Propensity Score based adjustment of the dataset. The formula for SMD is given below.\n",
    "\n",
    "\n",
    "$SMD = \\frac{\\bar X_{1} - \\bar X_{2}}{\\sqrt{(S_{1}^{2} + S_{2}^{2})/2}}$\n",
    "\n",
    "Here, $\\bar X_{1}$ and $\\bar X_{2}$ are the sample mean for the treated and control groups.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "massive-papua",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Visual Interpreter\n",
    "interpretation = causal_estimate_strat.interpret(method_name=\"propensity_balance_interpreter\")"
   ]
  },
  {
   "source": [
    "This plot shows how the SMD decreases from the unadjusted to the stratified units. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "id": "pleased-medicine",
   "metadata": {},
   "source": [
    "## Method 2: Propensity Score Matching\n",
    "\n",
    "We will be using propensity scores to match units in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "still-adobe",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                       \n",
      "─────(Expectation(y|W1,W3,Z1,W2,W4,Z0,W0))\n",
      "d[v₀]                                     \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,Z1,W2,W4,Z0,W0,U) = P(y|v0,W1,W3,Z1,W2,W4,Z0,W0)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W3+Z1+W2+W4+Z0+W0\n",
      "Target units: atc\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.9974377964538144\n",
      "\n",
      "Causal Estimate is 0.9974377964538144\n"
     ]
    }
   ],
   "source": [
    "causal_estimate_match = model.estimate_effect(identified_estimand,\n",
    "                                              method_name=\"backdoor.propensity_score_matching\",\n",
    "                                              target_units=\"atc\")\n",
    "print(causal_estimate_match)\n",
    "print(\"Causal Estimate is \" + str(causal_estimate_match.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "empirical-eating",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9974377964538144 in the expected value of the outcome [y], over the data distribution/population represented by the dataset.\n"
     ]
    }
   ],
   "source": [
    "# Textual Interpreter\n",
    "interpretation = causal_estimate_match.interpret(method_name=\"textual_effect_interpreter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ethical-advice",
   "metadata": {},
   "source": [
    "Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handed-essence",
   "metadata": {},
   "source": [
    "## Method 3: Weighting\n",
    "\n",
    "We will be using (inverse) propensity scores to assign weights to units in the data. DoWhy supports a few different weighting schemes:\n",
    "1. Vanilla Inverse Propensity Score weighting (IPS) (weighting_scheme=\"ips_weight\")\n",
    "2. Self-normalized IPS weighting (also known as the Hajek estimator) (weighting_scheme=\"ips_normalized_weight\")\n",
    "3. Stabilized IPS weighting (weighting_scheme = \"ips_stabilized_weight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "affected-student",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                       \n",
      "─────(Expectation(y|W1,W3,Z1,W2,W4,Z0,W0))\n",
      "d[v₀]                                     \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,Z1,W2,W4,Z0,W0,U) = P(y|v0,W1,W3,Z1,W2,W4,Z0,W0)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W3+Z1+W2+W4+Z0+W0\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.9941889177619327\n",
      "\n",
      "Causal Estimate is 0.9941889177619327\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "causal_estimate_ipw = model.estimate_effect(identified_estimand,\n",
    "                                            method_name=\"backdoor.propensity_score_weighting\",\n",
    "                                            target_units = \"ate\",\n",
    "                                            method_params={\"weighting_scheme\":\"ips_weight\"})\n",
    "print(causal_estimate_ipw)\n",
    "print(\"Causal Estimate is \" + str(causal_estimate_ipw.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "trained-print",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9941889177619327 in the expected value of the outcome [y], over the data distribution/population represented by the dataset.\n"
     ]
    }
   ],
   "source": [
    "# Textual Interpreter\n",
    "interpretation = causal_estimate_ipw.interpret(method_name=\"textual_effect_interpreter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "continental-colorado",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x576 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "interpretation = causal_estimate_ipw.interpret(method_name=\"confounder_distribution_interpreter\", fig_size=(8,8), font_size=12, var_name='W4', var_type='discrete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tropical-helmet",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}